Junior AI Engineer – Agentic AI & Enterprise AI

Role: Junior AI Engineer Agentic AI & Enterprise AI

Experience: 2 4 Years
Location: Santa Clara CA

Role Overview:

Seeking a Junior AI Engineer to develop and deploy next-generation Agentic AI and Generative AI solutions for enterprise customers. The ideal candidate has hands-on experience building LLM-powered applications RAG systems and AI agents with a strong software engineering foundation and a passion for leveraging Client AI ecosystem.

Key Responsibilities:

• Develop Agentic AI and LLM-based applications for enterprise use cases.
• Build and optimize Retrieval-Augmented Generation (RAG) solutions.
• Implement AI workflows using frameworks such as LangGraph CrewAI AutoGen LangChain or LlamaIndex.
• Deploy and integrate AI services using NVIDIA AI technologies including NIM Microservices and NVIDIA AI Enterprise.
• Develop scalable Python-based APIs services and integrations.
• Collaborate with architects data scientists and platform teams to deliver production-ready AI solutions.
• Contribute to reusable accelerators frameworks and best practices.

Required Qualifications:

• Bachelors or Masters degree in Computer Science AI Data Science or related field.
• 2 4 years of experience in software engineering machine learning or AI development.
• Strong Python programming skills.
• Experience building applications using LLMs and Generative AI technologies.
• Hands-on experience with RAG vector databases and prompt engineering.
• Experience with one or more frameworks: LangChain LangGraph CrewAI AutoGen or LlamaIndex.
• Familiarity with APIs microservices Git Docker and cloud platforms.

Preferred Qualifications:

• Experience with Client technologies such as NIM NeMo TensorRT-LLM Triton Inference Server or client AI Enterprise.
• Experience building multi-agent or Agentic AI solutions.
• Knowledge of Kubernetes MLOps and LLMOps.
• Experience deploying AI solutions on AWS Azure or GCP.

Technical Assessment (Required):

Candidates will be expected to demonstrate hands-on proficiency through a practical coding assessment covering:

• Development of a simple RAG application.
• Design of an Agentic AI or multi-agent workflow.
• API integration and tool-calling implementation.
• Prompt engineering and response evaluation.
• Deployment and optimization of an AI service (preferred: NVIDIA stack).

What Success Looks Like:

• Deliver production-ready AI solutions using enterprise-grade engineering practices.
• Successfully contribute to Agentic AI and GenAI implementations.
• Demonstrate proficiency across the NVIDIA Enterprise AI ecosystem.
• Build scalable secure and performant AI applications for enterprise customers.